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AI can add a reading layer to a feed. Design that layer so a generated summary, category, or extracted entity never obscures the original evidence.
Give the important decisions a name before choosing an implementation.
Keep original content and generated interpretation in different fields. A label or summary should carry the source item and revision it depends on. When a reviewer disagrees, the underlying evidence should still be available. This separation also lets you improve a task without pretending that the publisher revised its own material.
Define a small, explicit output contract before asking for a result. A categorizer can use an approved vocabulary and a review state; a summarizer can preserve uncertainty and identify missing evidence. Validate the shape, then evaluate whether the result is actually supported. These checks answer different questions and should not collapse into one success flag.
External feed text should not control your application’s permissions. Keep task instructions in trusted configuration and limit the enrichment worker to the operations it needs. A summary task can produce a draft without access to a publishing tool. Add capabilities only after the workflow has a clear reason and a review path.
Choose a narrow categorization task and a small set of representative items. Keep the source content visible beside the proposed label, including a route to leave the item unassigned. Compare suggestions with an agreed review standard and record the error types that matter. Only expand into summaries or more autonomous behavior after the initial task provides a measurable benefit to the people using the queue.
Design a bounded enrichment workflow for summaries and labels, with source evidence, review states, and sensible failure handling.
A project to think about: An editorial queue that suggests categories while keeping the original article one click away.
Open the complete guideExplore adjacent topics, then return to your own project with a clearer plan.